research-article

Using data mining techniques to explore security issues in smart living environments in Twitter

Authors:

Jose Ramon Saura,

Daniel Palacios-Marqués,

Domingo Ribeiro-SorianoAuthors Info & Claims

Volume 179, Issue C

Pages 285 - 295

https://doi.org/10.1016/j.comcom.2021.08.021

Published: 01 November 2021 Publication History

Abstract

In present-day in consumers’ homes, there are millions of Internet-connected devices that are known to jointly represent the Internet of Things (IoT). The development of the IoT industry has led to the emergence of connected devices and home assistants that create smart living environments. However, the continuously generated data accumulated by these connected devices create security issues and raise user’s privacy concerns. The present study aims to explore the main security issues in smart living environments using data mining techniques. To this end, we applied a three-sentence data mining analysis of 9,38,258 tweets collected from Twitter under the user-generated data (UGD) framework. First, sentiment analysis was applied using Textblob which was tested with support vector classifier, multinomial naïve bayes, logistic regression, and random forest classifier; as a result, the analyzed tweets were divided into those expressing positive, negative, and neutral sentiment. Next, a Latent Dirichlet Allocation (LDA) algorithm was applied to divide the sample into topics related to security issues in smart living environments. Finally, the insights were extracted by applying a textual analysis process in Python validated with the analysis of frequency and weighted percentage variables and calculating the statistical measure known as mutual information (MI) to analyze the identified n-grams (unigrams and bigrams). As a result of the research 10 topics were identified in which we found that the main security issues are malware, cybersecurity attacks, data storing vulnerabilities, the use of testing software in IoT, and possible leaks due to the lack of user experience. We discussed different circumstances and causes that may affect user security and privacy when using IoT devices and emphasized the importance of UGC in the processing of personal data of IoT device users.

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Dalal SManoharan PLilhore USeth BMohammed alsekait DSimaiya SHamdi MRaahemifar K(2023)Extremely boosted neural network for more accurate multi-stage Cyber attack prediction in cloud computing environmentJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-022-00356-912:1Online publication date: 23-Jan-2023
https://dl.acm.org/doi/10.1186/s13677-022-00356-9
Huq SAlshayban AHe ZMalek S(2023)#A11yDev: Understanding Contemporary Software Accessibility Practices from Twitter ConversationsProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581455(1-18)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581455
Kaur RGabrijelčič DKlobučar T(2023)Artificial intelligence for cybersecurityInformation Fusion10.1016/j.inffus.2023.10180497:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.inffus.2023.101804
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Using data mining techniques to explore security issues in smart living environments in Twitter

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Published In

cover image Computer Communications

Computer Communications Volume 179, Issue C

Nov 2021

319 pages

ISSN:0140-3664

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 November 2021

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Dalal SManoharan PLilhore USeth BMohammed alsekait DSimaiya SHamdi MRaahemifar K(2023)Extremely boosted neural network for more accurate multi-stage Cyber attack prediction in cloud computing environmentJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-022-00356-912:1Online publication date: 23-Jan-2023
https://dl.acm.org/doi/10.1186/s13677-022-00356-9
Huq SAlshayban AHe ZMalek S(2023)#A11yDev: Understanding Contemporary Software Accessibility Practices from Twitter ConversationsProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581455(1-18)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581455
Kaur RGabrijelčič DKlobučar T(2023)Artificial intelligence for cybersecurityInformation Fusion10.1016/j.inffus.2023.10180497:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.inffus.2023.101804
Pattnaik NLi SNurse J(2023)Perspectives of non-expert users on cyber security and privacyComputers and Security10.1016/j.cose.2022.103008125:COnline publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1016/j.cose.2022.103008
Mahaini MLi S(2023)Cyber Security Researchers on Online Social Networks: From the Lens of the UK’s ACEs-CSR on TwitterSecurity and Privacy in Social Networks and Big Data10.1007/978-981-99-5177-2_8(129-148)Online publication date: 14-Aug-2023
https://dl.acm.org/doi/10.1007/978-981-99-5177-2_8
Paulo MMiguéis VPereira I(2022)Leveraging email marketingExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.117974207:COnline publication date: 30-Nov-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.117974
Zhang ZSong X(2022)An Exploratory Study on Utilising the Web of Linked Data for Product Data MiningSN Computer Science10.1007/s42979-022-01415-34:1Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1007/s42979-022-01415-3

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